With ever-increasing demand for wireless communication and broadband services, there is an ongoing evolution of Third Generation (3G) and Fourth Generation (4G) cellular networks like High Speed Packet Access (HSPA), Evolution-Data Optimized (EV-DO), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), etc., to support ever-increasing performance with regard to capacity, peak bit rates and coverage. In case of a mobile communication environment, such as Third Generation Partnership Project's (3GPP) LTE network, the Evolved Universal Terrestrial Radio Access (EUTRA) or Evolved Universal Terrestrial Radio Access Network (E-UTRAN) air interface for LTE may support wireless broadband data service at a rate of up to 300 Mbps in the downlink and 75 Mbps in the uplink. Due to increasing popularity of multimedia communications over wireless networks, emerging technologies like Multiple Input Multiple Output (MIMO) have been widely used in modern mobile communication environment (e.g., the LTE network) to meet the demand for higher data rate and better cell coverage without increasing average transmit power or frequency bandwidth. MIMO also improves uplink/downlink peak rates, cell coverage, as well as cell throughput. In MIMO, multiple spatial layers are constructed to deliver multiple data streams on a given frequency-time resource, thereby linearly increasing the channel capacity.
It is noted here that MIMO is a spatial diversity scheme in which multiple antennas are used at the transmitter and/or the receiver end. Like time diversity (different time slots and channel coding) and frequency diversity (different channels, spread spectrum, and Orthogonal Frequency Division Multiplexing (OFDM)), spatial diversity mode can also make radio communication more robust, even with varying channels. A multiple antenna based spatial diversity technology can also be used to increase the data rate (known as “spatial multiplexing”). In spatial multiplexing, data may be divided into separate streams or layers; the streams are then transmitted independently via separate antennas. When the data rate is to be increased for a single User Equipment (UE), the MIMO scheme is referred to as Single User MIMO (SU-MIMO). On the other hand, when individual data streams are assigned to multiple users (or UE's), it is called Multi User MIMO (MU-MIMO). The MU-MIMO may be useful in the uplink because the complexity on the UE side can be kept at a minimum by using only one transmit antenna per UE.
FIG. 1 illustrates an exemplary MIMO signal detection system 10. As shown in FIG. 1, MIMO transmitted signals/symbols 12 may travel through a MIMO channel 14 and be received (as indicated by arrow 16) at a receiver/detector 17. The output of the detector 17 may comprise detected signals 18 corresponding to the transmitted signals 12. For ease of discussion and sake of simplicity, the detector 17 is shown to detect a single MIMO signal using a Frequency Domain (FD) equalization and demodulation unit 19 (conveniently referred to as “equalization unit” in the context of discussion of FIG. 1), a decoder 20, a signal regenerator 21, and an interference canceller 22. In the context of FIG. 1, the components 19 through 22 may perform detection of a single MIMO layer signal. In practical implementations (as discussed in more detail with reference to the receiver 62 in FIG. 4), however, the detector 17 may include multiple parallel stages for interference cancellation, and each stage may in turn include multiple layer-specific MIMO signal detectors for each received MIMO layer signal. Each such MIMO signal detector may include an equalization unit (similar to the unit 19), a decoder unit (similar to the decoder 20), and a signal regenerator unit (similar to the signal regenerator 21). Outputs of all MIMO signal detectors in a stage may be received at an interference canceller unit (similar to the interference canceller 22) to provide interference cancellation for the received MIMO signals 16. A more detailed discussion of MIMO signal detection through parallel interference cancellation is provided later hereinbelow with reference to FIG. 4.
It is understood that each MIMO signal may carry a transport block consisting of a number of information bits. The information bits in a transport block are coded using a turbo code to produce a sequence of encoded bits, often referred to as a codeword. The encoded bits may be interleaved or scrambled to produce a sequence of channel bits. The channel bits are mapped to modulation symbols, which constitute a MIMO signal. It is noted here that the terms “signal” and “symbol” may be used interchangeably herein for the sake of convenience and ease of discussion, even though a MIMO transmitted signal may contain more than one symbol. Furthermore, as used herein, the term “symbol” may refer to information content transmitted by a single antenna in a single transmission, although each such “symbol” may include a plurality of encoded bits and multiple such “symbols” may be serially concatenated as part of the single transmission from the antenna. In case of an LTE network, for example, such transmission may include a radio frame having one or more subframes (not shown). Also in case of an LTE network, for example, the MIMO channel 14 may receive the MIMO signals 12 transmitted at different MIMO layers by, for example, different UE's. The base station or evolved Node-B (eNodeB or eNB) (not shown) in the LTE network may include the receiver/detector 17 to detect these MIMO signals received from the channel 14. Because MIMO signals/symbols 12 are transmitted via the same channel 14, transmissions using cross components not equal to zero (0) may mutually influence one another and give rise to Inter Symbol Interference (ISI). If channel transmission matrix (or frequency response) H of the MIMO channel 14 is known, these cross components can be estimated at the receiver (which could be an eNB or a UE or both, depending on MIMO implementation) using the known symbols transmitted from the multiple transmit antennas. After obtaining an estimate of the channel transmission matrix H, the receiver reports the channel status to the transmitter via a special feedback channel (e.g., in case of eNB as a MIMO transmitter and UE as a MIMO receiver, the channel feedback may be sent from a UE to a base station via the Physical Uplink Control Channel (PUCCH) or the Physical Uplink Shared Channel (PUSCH) in 3G and 4G cellular networks), thereby making it possible for the transmitter to optimize MIMO transmissions by adapting to changing channel conditions. In any event, the receiver preferably initially performs equalization before carrying out demodulation and decoding.
In the context of FIG. 1, the receiver 17 may be a turbo equalization receiver that combines channel equalization and decoding in an iterative detection scheme, which provides soft symbol (i.e., computed symbol) based interference cancellation through interference canceller 22. In a soft symbol based interference cancellation scheme, the amount of interference cancellation depends on the reliability of the symbols estimated by the signal regenerator 21. These soft estimated symbols are formed or computed using the decoder output Log Likelihood Ratios (LLRs) about the encoded bits. In a turbo equalizer, equalization and decoding operations are repeated several times on the same set of received symbols. Each iteration (of equalization and decoding operations) is carried out by a module supplied with channel observations H as well as with a priori information on the coded bits (in the transmitted symbols) in the LLR form delivered by the decoder unit of the previous module. For the sake of clarity, in the discussion herein, the term “turbo equalizer” (or “turbo equalization receiver”) may be used to refer to the receiver or detector 17 (or similar such entity such as, e.g., the receiver 62 in FIG. 4) as a whole, whereas the term “equalizer” may be used to refer to only the equalization part (e.g., the equalization unit 19 in FIG. 1) of the turbo equalizer.
It is understood that if the symbols are estimated very reliably (e.g., by the signal regenerator 21 using outputs from the decoder 20), then interference can be largely removed. In contrast, if the estimated symbols are not very reliable, then only a small portion of the interference is removed and re-equalization may be needed. A turbo equalizer may be a multi-stage Parallel Interference Cancellation (PIC) receiver in which the received signal may be re-equalized at a successive stage after interference cancellation at the initial stage. In this case, the equalization weights (or coefficients) applied in the equalizer (e.g., similar to the equalization unit 19) of that successive stage may be adaptive to the residual interference characteristics. With updated equalization weights optimized for the characteristics of the residual interference, the equalizer can be more effective (during re-equalization) in suppressing whatever dominant interference is left after initial interference cancellation.
However, adapting equalization weights in every turbo equalization receiver stage may consume significant computation resources. Typically, equalization weight computation involves matrix inversion or solving linear equation using, e.g., the Gauss-Seidel algorithm. The size of the matrix that needs to be inverted is nR by nR, where nR is the number of receive antennas (e.g., in case of FIG. 1, nR is the number of receive antennas receiving the MIMO signals 16). Furthermore, equalization weights may be needed for each subcarrier and each MIMO layer. However, a number of neighboring subcarriers may share the same equalization weights.